Computer vision-based laser communication system for robust optical beam tracking and alignment.

Area of Science:

• Optical Communications
• Computer Vision
• Robotics

Background:

• Free-space optical (FSO) communication offers high-speed data transmission but requires precise beam alignment.
• Maintaining stable optical alignment is critical for reliable FSO system performance.
• Existing tracking systems often struggle with dynamic environmental factors.

Purpose of the Study:

• To develop a computer vision-assisted system for real-time, robust optical alignment in FSO communication.
• To enhance the reliability and accuracy of beam tracking in challenging conditions.
• To enable practical FSO applications requiring adaptive beam steering.

Main Methods:

• Integration of a lightweight Convolutional Neural Network (CNN) for laser spot detection.
• Utilization of a Kalman filter for accurate tracking and prediction.
• Implementation of a closed-loop feedback mechanism for real-time beam adjustment.
• Deployment on an embedded Jetson Xavier NX platform for low-latency operation.

Main Results:

• Achieved 98.5% tracking accuracy in real-time optical alignment.
• Demonstrated reliable 1 Gbps data transmission over 2 km distances.
• Showcased consistent performance in adverse conditions like fog, wind, motion blur, and glare.
• Significantly reduced bit error rates and improved signal stability compared to conventional methods.

Conclusions:

• The computer vision-assisted system provides a practical solution for robust FSO beam alignment.
• The system's low-latency and efficient operation make it suitable for UAV and satellite communication.
• Integrating computer vision enhances FSO system performance, especially in dynamic environments.
• Future research directions include predictive tracking and multi-sensor fusion for extreme conditions.